Neo4j

Hi, After Refactoring Graphs the Node Labels are not updated (I refreshed the browser - Netscape) to achieve this result. It would be great to have this information updated automatically or have a refresh button to do the task. Regards, I made other changes and the nodes refreshed. ... View more

Hello Community, I hope you guys are doing well! I've got my graphDB designed and looking into physically printing them(scrim vinyl, gloss vinyl, matte paper, glass paper, semi glass paper, etc..) to present and post it on my clients conference room. I'd really appreciate if you guys could suggest best way to print graphDB and steps to follow!!! Thank you for you time in advance. Respectfully, Lee ... View more

In April 2020, the APOC standard library added procedures that wrap the NLP APIs of each of the big cloud providers - AWS, GCP, and Azure. These procedures extract text from a node property and then send that text to APIs that extract entities, key phrases, categories, or sentiment. We’re going to use the GCP Entity Extraction procedures on our articles. The GCP NLP API returns Wikipedia pages for entities where those pages exist. Before we do that we’ll need to create an API key that has access to the Natural Language API. Assuming that we’re already created a GCP account, we can generate a key by following the instructions at console.cloud.google.com/apis/credentials. Once we’ve created a key, we’ll create a parameter that contains it: :params key => ("<insert-key-here>") We’re going to use the apoc.nlp.gcp.entities.stream procedure, which will return a stream of entities found for the text content contained in a node property. Before running this procedure against all of the articles, let’s run it against one of them to see what data is returned: MATCH (a:Article {uri: "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"}) CALL apoc.nlp.gcp.entities.stream(a, { nodeProperty: 'body', key: $key }) YIELD node, value SET node.processed = true WITH node, value UNWIND value.entities AS entity RETURN entity LIMIT 5; Table 8. Results entity {name: "RethinkDB", salience: 0.47283632, metadata: {mid: "/m/0134hdhv", wikipedia_url: "https://en.wikipedia.org/wiki/RethinkDB"}, type: "ORGANIZATION", mentions: [{type: "PROPER", text: {content: "RethinkDB", beginOffset: -1}}, {type: "PROPER", text: {content: "RethinkDB", beginOffset: -1}}, {type: "PROPER", text: {content: "RethinkDB", beginOffset: -1}}, {type: "PROPER", text: {content: "RethinkDB", beginOffset: -1}}, {type: "PROPER", text: {content: "pemThe RethinkDB", beginOffset: -1}}]} {name: "connection", salience: 0.04166339, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "connection", beginOffset: -1}}, {type: "COMMON", text: {content: "connection", beginOffset: -1}}]} {name: "work", salience: 0.028608896, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "work", beginOffset: -1}}]} {name: "projects", salience: 0.028608896, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "projects", beginOffset: -1}}]} {name: "database", salience: 0.01957906, metadata: {}, type: "OTHER", mentions: [{type: "COMMON", text: {content: "database", beginOffset: -1}}]} Each row contains a name property that describes the entity. salience is an indicator of the importance or centrality of that entity to the entire document text. Some entities also contain a Wikipedia URL, which is found via the metadata.wikipedia_url key. The first entity, RethinkDB, is the only entity in this list that has such a URL. We’re going to filter the rows returned to only include ones that have a Wikipedia URL and we’ll then connect the Article nodes to the WikipediaPage nodes that have that URL. Let’s have a look at how we’re going to do this for one article: MATCH (a:Article {uri: "https://dev.to/lirantal/securing-a-nodejs--rethinkdb--tls-setup-on-docker-containers"}) CALL apoc.nlp.gcp.entities.stream(a, { nodeProperty: 'body', key: $key }) (1) YIELD node, value SET node.processed = true WITH node, value UNWIND value.entities AS entity (2) WITH entity, node WHERE not(entity.metadata.wikipedia_url is null) (3) MERGE (page:Resource {uri: entity.metadata.wikipedia_url}) SET page:WikipediaPage (4) MERGE (node)-[:HAS_ENTITY]->(page) 1 node is the article and value contains the extracted entities 2 Only include entities that have a Wikipedia URL 3 Find a node that matches the Wikipedia URL. Create one if it doesn’t already exist. 4 Create a HAS_ENTITY relationship between the Article node and WikipediaPage We can see how running this query connects the article and taxonomy sub graphs by looking at the following Neo4j Browser visualization: Now we can run the entity extraction technique over the rest of the articles with help from the apoc.periodic.iterate procedure again: CALL apoc.periodic.iterate( "MATCH (a:Article) WHERE not(exists(a.processed)) RETURN a", "CALL apoc.nlp.gcp.entities.stream([item in $_batch | item.a], { nodeProperty: 'body', key: $key }) YIELD node, value SET node.processed = true WITH node, value UNWIND value.entities AS entity WITH entity, node WHERE not(entity.metadata.wikipedia_url is null) MERGE (page:Resource {uri: entity.metadata.wikipedia_url}) SET page:WikipediaPage MERGE (node)-[:HAS_ENTITY]->(page)", {batchMode: "BATCH_SINGLE", batchSize: 10, params: {key: $key}}) YIELD batches, total, timeTaken, committedOperations RETURN batches, total, timeTaken, committedOperations; Table 9. Results batches total timeTaken committedOperations 4 31 29 31 This is a companion discussion topic for the original entry at https://neo4j.com/developer/graph-data-science/build-knowledge-graph-nlp-ontologies/ ... View more

Hi there, trying to download from Debian repo and getting this: curl -I http://debian.neo4j.org/neotechnology.gpg.key HTTP/1.1 503 Service Unavailable: Back-end server is at capacity Connection: keep-alive Same for anything under debian.neo4j.org. It's been like this since Friday afternoon. Cheers, Igor ... View more

In one of the examples, there's an example of Martin Sheen and Charlie Sheen related by the FATHER relationship. This is great, except these actors aren't in the Example DB and there are no FATHER relationships. (Marriage relationships too.) It would be nice to add that data to the sample Movie DB. Example page where the Sheens are used: Neo4j Graph Database Platform Maps - Neo4j Cypher Manual This section describes how to use maps in Cyphers. In addition, it would be also interesting to add these queries: Actors who are related to Martin Sheen but don't share the last name : Emilio Estevez Actors who share the last name of Sheen but aren't related: Michael Sheen. Another interesting property that could be added, would be the birth name of an actor. Another suggestion that I made elsewhere is including box office revenue and cost of making a film. Then one can make interesting queries about who was in movies that made the most money (in absolute terms and ROI.). Also nice would be showing how to format money amounts for display (e.g. $ and commas between 000). Also.... another issue I have with the URL above, is it uses some notation that's not really used in Neo4J: {nrOfMovies -> 2, name -> "Martin Sheen"} instead of {nrOfMovies : 2, name : "Martin Sheen"} ... View more

We are interested in importing the activities for the athlete who is logged in. That endpoint takes the following parameters: We’re interested in per_page (where we can define the number of activities returned per call to the endpoint) and after (where we can tell the API to only return results after a provided epoch timestamp). Let’s imagine that we have more activities that we can return in one request to the API. We’ll need to paginate to retrieve all our activities and import them into Neo4j. Before we paginate the API, let’s first learn how to import one page worth of activities into Neo4j. The following query will return activities starting from the earliest timestamp: WITH after AS 0 WITH 'https://www.strava.com/api/v3/athlete/activities?after=' + after AS uri CALL apoc.load.jsonParams(uri, {Authorization: $stravaToken}, null) YIELD value CREATE (run:Run {id: value.id}) SET run.distance = toFloat(value.distance), run.startDate = datetime(value.start_date_local), run.elapsedTime = duration({seconds: value.elapsed_time}) We create a node with the label Run for each activity and set a few properties, as well. The most interesting one for this example is startDate which we will pass to the after parameter later on. This query will load the first 30 activities, but what if we want to get the next 30? We can change the first line of the query to find the most recent timestamp of any of our Run nodes and then pass that to the API. If there aren’t any Run nodes, then we can use a value of 0 like in the query below. OPTIONAL MATCH (run:Run) WITH run ORDER BY run.startDate DESC LIMIT 1 WITH coalesce(run.startDate.epochSeconds, 0) AS after WITH 'https://www.strava.com/api/v3/athlete/activities?after=' + after AS uri CALL apoc.load.jsonParams(uri, {Authorization: $stravaToken}, null) YIELD value CREATE (run:Run {id: value.id}) SET run.distance = toFloat(value.distance), run.startDate = datetime(value.start_date_local), run.elapsedTime = duration({seconds: value.elapsed_time}) We could continue to run this query manually, but it’s about time that we automated it. This is a companion discussion topic for the original entry at https://neo4j.com/developer/guide-import-json-rest-api/ ... View more

The line below say everything, it means being able to manipulate CONSTRAINTS commands as we do with PRIVILEGES. SHOW CONSTRAINT AS [DROP] COMMANDS Thank you for this amazing tool is Neo4j ... View more

Hey Guys, Not sure if it has already been reported but there is a small error in the training example: image|690x477 Captured in that image you can see for the Importing Data training; The create index statement switches around the on and for in the statement. Cheers, Kriek ... View more

Bloom uses Command-Click (on the Mac) to make multiple selection of Nodes and Relationships. For the Mac, it's standard to use Shift-Click instead. [added] created this as an issue: Bloom Should use Shift-Click to make multiple selections · Issue #26 · neo4j-devtools/neo4j-bloom · GitHub ... View more

I created a project in Neo4j Sandbox, but when I use the invitation feature to share, my colleagues find that the sandbox hangs at the intermediate webpage that says "Neo4j Sandbox Browser: Attempting automatic signin to Neo4j Sandbox" . To test this on the receiving end, I had a friend share a project with me, and then I experienced the same problem, stuck on the intermediate webpage. A user can only view the projects that the user has personally created. Viewing a project that someone else shared doesn't work. ... View more

I conduct training sessions on Neo4j. For my sessions, I wish to include more of Data Analytics Use Cases. I have included few Recommender systems but wish to include more use cases in different spectrum. Request you to help me with the same or connect me to the right practitioner. I also have a request, can you suggest scholars who can come to India and present their research paper in one of the Data Science Congress. ... View more

I can't show one node funding another with a specific amount of money because I can't have a "$" symbol in the relationship name!? Also, I'm not allowed to start a relationship name with a number for any reason? I'm still a novice neo4j but this seems to be a show stopper for some (many?) graph database projects. ... View more

I am trying to use get started with data science algorithms on our Neo4j database. After entering my name and email to download Neo4j Desktop, this page (https://neo4j.com/download-thanks-desktop/?edition=desktop&flavour=osx&release=1.3.11&offline=true) has a MacOS link (https://s3-eu-west-1.amazonaws.com/dist.neo4j.org/neo4j-desktop/mac-offline/Neo4j%20Desktop-1.3.11.dmg?x-amz-security-token=...) which says only, NoSuchKeyThe specified key does not exist.neo4j-desktop/mac-offline/Neo4j Desktop-1.3.11.dmgC9BD67DBF6FA9BC0BHe1Tt0g7bdlt9WQ0bhwK+Lr04qG5j2rDgIJ5IfskiYkRXJjDrNpSkKdcVGEuthdHWaPYKiUssA= ... View more

Neo4j Windows desktop. version Version: 1.3.4 I created [ bom2.cypher ] DB from here: (Bill of Materials sample data · GitHub) Then I ran cypher from here Bill of Materials in Neo4j – Max De Marzi MATCH path = (t:Part {id:"120-001"})<-[:BELONGS_TO*0..]-(:Part) WHERE last(nodes(path)).cost is null WITH t, last(nodes(path)) AS missing_cost WITH t, missing_cost, SIZE(COLLECT(missing_cost)) AS need_cost MATCH path = (missing_cost)<-[:BELONGS_TO*0..]-(p:Part) WITH t, missing_cost, need_cost, COLLECT(DISTINCT p) AS parts WHERE SINGLE(x IN parts WHERE COALESCE(x.cost, x.part_cost) IS NULL) MATCH (missing_cost)<-[r:BELONGS_TO]-(p:Part) WITH missing_cost, SUM(r.qty * COALESCE(p.cost, p.part_cost)) AS part_cost WHERE missing_cost.part_cost is NULL SET missing_cost.part_cost = part_cost Nothing happened. Run again and again. Nothing I added PROFILE before the first MATCH and the command ran and the "set" at the end worked. What is the reason? ... View more

I have a Node with a two letter Name "HR" which is short for Human Resources. When I search for it in Bloom, Bloom refuses to expand and complete it, even though it's an exact match. I'm guessing that you have to type at least 3 characters before Bloom gives you a hint. This makes some sense, as the user would get an unhelpful huge list of possible matches after typing one or two letters. I think you should allow 1 or 2 character searches to complete IF it's an exact match. ... View more

The LOAD CSV command reads all values as a string. No matter how the value appears in a file, it will be loaded as a string with LOAD CSV . So, before we import, we want to ensure we convert any values that are non-string. There are a variety of conversion functions in Cypher. The ones we will use for this exercise are as follows: toInteger(): converts a value to an integer. toFloat(): converts a value to a float (in this case, for monetary amounts). datetime(): converts a value to a datetime. We will look at the values in each CSV file to determine what needs to be converted. Products.csv The values in the products.csv files are for product ID, product name, and unit cost. Product ID looks like an integer value that increases with each row, so we can convert this to an integer using the toInteger() function in Cypher. Product name can remain a string since it consists of characters. The final column is the product unit cost. Though the sample values from our inspection are all whole numbers, we know that monetary amounts often have decimal place values, so we will convert these values to floats using the toFloat() function. We can see the Cypher to handle all of these conversions below; however, we are still not loading the values into Neo4j yet. We are just viewing the CSV files with converted values. LOAD CSV FROM 'file:///desktop-csv-import/products.csv' AS row WITH toInteger(row[0]) AS productId, row[1] AS productName, toFloat(row[2]) AS unitCost RETURN productId, productName, unitCost LIMIT 3 Note that we are using collection positions (row[0], row[1], row[2]) to refer to the columns in the row and improve readability by using aliases to reference them in the return. In a file that has no headers, this is how to reference values in each position. Orders.csv The values in the orders.csv (per the column names) are for orderID, orderDate, and shipCountry. Again, we can evaluate the values and determine any conversions to apply. OrderID looks like an integer, so we can convert that using the toInteger() function. The orderDate column is certainly in a date format and will require us to format it using the datetime() function. Finally, the shipCountry values are characters, so we can leave that as a string. If you are using a version of Neo4j prior to 3.4, the datetime() function will not be available. That function was released in 3.4 and is supported from that version forward. Just as we did with the last CSV file, let us look at the results of these conversions without importing the data. LOAD CSV WITH HEADERS FROM 'file:///desktop-csv-import/orders.csv' AS row WITH toInteger(row.orderID) AS orderId, datetime(replace(row.orderDate,' ','T')) AS orderDate, row.shipCountry AS country RETURN orderId, orderDate, country LIMIT 5 There was one tricky thing with this CSV in the orderDate column. Neo4j’s datetime uses the ISO 8601 format which uses the delimiter T between the date and time values. The CSV file does not have the 'T' joining the date and time values but has a space between them instead. We used the replace() function to change the space to the character 'T' and get the string into the expected format. Then, we wrapped the datetime() function around that to convert the changed string to a datetime value. Order-details.csv The values in the order-details.csv (from column names) are for productID, orderID, and quantity. Let us look at which ones need to be converted. Our product ID is also from our products.csv file, where we converted that value to an integer. We will do the same here to ensure we match formats. The order ID field contains values from our orders.csv file, so we will match our previous conversion and translate this field to an integer, as well. The quantity field in this file is a numeric value. We can convert this to an integer with the toInteger() function we have been using. The results of these conversions are in the code below. Remember that we still are not loading any data yet. LOAD CSV WITH HEADERS FROM 'file:///desktop-csv-import/order-details.csv' AS row WITH toInteger(row.productID) AS productId, toInteger(row.orderID) AS orderId, toInteger(row.quantity) AS quantityOrdered RETURN productId, orderId, quantityOrdered LIMIT 8 This is a companion discussion topic for the original entry at https://neo4j.com/developer/desktop-csv-import/ ... View more

APOC is Neo4j’s utility library for handling data import, as well as data transformations and manipulations. From converting values to altering the data model, this library can manage it all, allowing you to combine and chain procedures in order to get exactly the results you are looking for. For data import, APOC offers several options depending on your data source and format. It can import files or data from a URL in CSV, JSON, or XML formats, as well as loading data straight from a database (using JDBC). When you call these procedures, you can pass in the data source and use other procedures to manipulate data or regular Cypher to insert or update to the database. There are also procedures for batching data, adding wait/sleep commands, and handling large data sets or temperamental data sources. The transformation procedures in this library are nearly endless, allowing the developer to process dynamic labels or relationships, correct/skip null or empty values, format dates or other values, generate hashes, and handle other tricky data scenarios. If you are in need of a way for flexible and custom data handling, APOC could be the way to go. The downside to using this library for complicated scenarios is that it may result in many lines of code to handle multiple data transformations. This is a companion discussion topic for the original entry at https://neo4j.com/developer/relational-to-graph-import/ ... View more

When I’m looking at community posts, I can’t read content of posts because the font is light gray against white. I can see the titles, navigate, and the post I’m writing. Firefox and Edge also have this problem. This is on my iPhone 6S, iOS 14.2, Chrome and Safari. (Not sure how to get their versions.) More clues. This only happens after I login into my Neo4J account. When I'm not logged in, the background is dark, so the light text is visible. Also... no problems on Mac OSX with Chrome. ... View more

What if we want to filter our articles based on these temporal values. Let’s start by finding the articles that were published on 1st June 2019. The following query does this: MATCH (article:Article) WHERE article.datePublished = date({year: 2019, month: 6, day: 1}) RETURN article.title AS title, article.created AS created, article.datePublished AS datePublished, article.readingTime AS readingTime Table 3. Results title created datePublished readingTime "Cypher Basics I" 2019-06-01T18:40:32.142+01:00 2019-06-01 P0M0DT135S What about if we want to find all the articles published in June 2019? We might write the following query to do this: MATCH (article:Article) WHERE article.datePublished = date({year: 2019, month: 6}) RETURN article.title AS title, article.created AS created, article.datePublished AS datePublished, article.readingTime AS readingTime If we run this query we’ll get the following results: Table 4. Results title created datePublished readingTime "Cypher Basics I" 2019-06-01T18:40:32.142+01:00 2019-06-01 P0M0DT135S This doesn’t seem right - what about the Cypher Basics II article that was published on 2nd June 2019? The problem we have here is that date({year: 2019, month:6}) returns 2019-06-01 , so we’re only finding articles published on 1st June 2019. We need to tweak our query to find articles published between June 1st 2019 and July 1st 2019. The following query does this: MATCH (article:Article) WHERE date({year: 2019, month: 7}) > article.datePublished >= date({year: 2019, month: 6}) RETURN article.title AS title, article.created AS created, article.datePublished AS datePublished, article.readingTime AS readingTime Table 5. Results title created datePublished readingTime "Cypher Basics I" 2019-06-01T18:40:32.142+01:00 2019-06-01 P0M0DT135S "Cypher Basics II" 2019-06-02T10:23:32.122+01:00 2019-06-02 P0M0DT150S What about if we want to filter based on the created property, which stores Datetime values? We need to take the same approach when filtering Datetime values as we did with Date values. The following query finds the articles created after July 2019: MATCH (article:Article) WHERE article.created > datetime({year: 2019, month: 7}) RETURN article.title AS title, article.created AS created, article.datePublished AS datePublished, article.readingTime AS readingTime Table 6. Results title created datePublished readingTime "Dates, Datetimes, and Durations in Neo4j" 2019-09-25T06:04:39.072Z 2019-09-25 P0M0DT210S And finally filtering durations. We might be interested in finding articles that can be read in 3 minutes or less. We’ll start with the following query: MATCH (article:Article) WHERE article.readingTime <= duration("PT3M") RETURN article.title AS title, article.created AS created, article.datePublished AS datePublished, article.readingTime AS readingTime If we execute that query we’ll see the following output: Neo.ClientError.Statement.SyntaxError: Type mismatch: expected Float, Integer, Point, String, Date, Time, LocalTime, LocalDateTime or DateTime but was Duration (line 2, column 29 (offset: 52)) "WHERE article.readingTime < duration("PT3M")" ^ If we want to compare durations we need to do that comparison by adding those durations to dates. We don’t really care about dates for our query so we’ll just use the current time to work around this issue. We can get the current time by calling the datetime() function. Our updated query reads like this: MATCH (article:Article) WHERE datetime() + article.readingTime <= datetime() + duration("PT3M") RETURN article.title AS title, article.created AS created, article.datePublished AS datePublished, article.readingTime AS readingTime Table 7. Results title created datePublished readingTime "Cypher Basics I" "01 June 2019 18:40" "01 June 2019" "02:15" "Cypher Basics II" "02 June 2019 10:23" "02 June 2019" "02:30" This is a companion discussion topic for the original entry at https://neo4j.com/developer/cypher/dates-datetimes-durations/ ... View more